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AITeamUIT/eval-gliner2-ner-setconsistent

Set-consistent — 4 bo NER, 18 run base model: fastino/gliner2-multi-v1 train: --loss set (mac dinh cua train.py cho moi tham so set) eval: --threshold 0.5 --use-desc --schema-case original --no-extra-val --loss set tu bat learnable null anchor trong scorer, nen checkpoint decode o threshold 0.5 — khac 0.7 cua bang loss-ablation 5 config. Khac biet co chu dich, khong phai threshold tuning. Ngan sach tung bo bo repo nhan split seeds epochs batch fin… See the full description on the dataset page: https://huggingface.co/datasets/AITeamUIT/eval-gliner2-ner-setconsistent.

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Set-consistent — 4 bo NER, 18 run

  • base model: fastino/gliner2-multi-v1
  • train: --loss set (mac dinh cua train.py cho moi tham so set)
  • eval: --threshold 0.5 --use-desc --schema-case original --no-extra-val
--loss set tu bat learnable null anchor trong scorer, nen checkpoint decode o threshold 0.5 — khac 0.7 cua bang loss-ablation 5 config. Khac biet co chu dich, khong phai threshold tuning.

Ngan sach tung bo

boreponhansplitseedsepochsbatch
finquynong/fin4test5816
mit_restaurantquynong/mit_restaurant8test5616
conll2003quynong/conll20034test5616
ontonotes5quynong/ontonotes518test3316

Ket qua (%, mean +/- std tren seed)

modebon seedsmacro F1micro F1micro Pmicro R
lenientfin545.83 +/- 8.0880.93 +/- 2.4085.2977.4
lenientmit_restaurant588.14 +/- 0.3889.09 +/- 0.2590.1988.02
lenientconll2003588.99 +/- 0.7890.29 +/- 0.8090.6989.89
lenientontonotes5381.13 +/- 0.2490.53 +/- 0.1089.4291.68
strictfin540.56 +/- 5.9075.41 +/- 1.8679.5572.06
strictmit_restaurant581.46 +/- 0.4482.45 +/- 0.3583.4781.45
strictconll2003586.91 +/- 0.7488.64 +/- 0.8089.0588.25
strictontonotes5376.96 +/- 0.4087.42 +/- 0.1586.3488.52

Dong cho tab:macro-f1 (strict macro F1)

latex
\textbf{Set-consistent (ours)} & $ 86.91_{\pm 0.74} $ & $ 76.96_{\pm 0.40} $ & $ 40.56_{\pm 5.90} $ & $ 81.46_{\pm 0.44} $ & \textbf{71.47} \\

Checkpoint tung run

generated 2026-08-22T09:38:00